{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython\n",
    "%matplotlib inline\n",
    "from prophet import Prophet\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import logging\n",
    "import warnings\n",
    "\n",
    "logging.getLogger('prophet').setLevel(logging.ERROR)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8395cd1a302349a6b766e256fff7aa02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n",
    "m = Prophet()\n",
    "m.fit(df)\n",
    "future = m.make_future_dataframe(periods=366)\n",
    "\n",
    "from prophet.diagnostics import cross_validation\n",
    "df_cv = cross_validation(\n",
    "    m, '365 days', initial='1825 days', period='365 days')\n",
    "cutoff = df_cv['cutoff'].unique()[0]\n",
    "df_cv = df_cv[df_cv['cutoff'].values == cutoff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Loading required package: Rcpp\n",
      "\n",
      "R[write to console]: Loading required package: rlang\n",
      "\n",
      "R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(prophet)\n",
    "df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n",
    "m <- prophet(df)\n",
    "future <- make_future_dataframe(m, periods=366)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross validation\n",
    "\n",
    "Prophet includes functionality for time series cross validation to measure forecast error using historical data. This is done by selecting cutoff points in the history, and for each of them fitting the model using data only up to that cutoff point. We can then compare the forecasted values to the actual values. This figure illustrates a simulated historical forecast on the Peyton Manning dataset, where the model was fit to an initial history of 5 years, and a forecast was made on a one year horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "input_hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(facecolor='w', figsize=(10, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(m.history['ds'].values, m.history['y'], 'k.')\n",
    "ax.plot(df_cv['ds'].values, df_cv['yhat'], ls='-', c='#0072B2')\n",
    "ax.fill_between(df_cv['ds'].values, df_cv['yhat_lower'],\n",
    "                df_cv['yhat_upper'], color='#0072B2',\n",
    "                alpha=0.2)\n",
    "ax.axvline(x=pd.to_datetime(cutoff), c='gray', lw=4, alpha=0.5)\n",
    "ax.set_ylabel('y')\n",
    "ax.set_xlabel('ds')\n",
    "ax.text(x=pd.to_datetime('2010-01-01'),y=12, s='Initial', color='black',\n",
    "       fontsize=16, fontweight='bold', alpha=0.8)\n",
    "ax.text(x=pd.to_datetime('2012-08-01'),y=12, s='Cutoff', color='black',\n",
    "       fontsize=16, fontweight='bold', alpha=0.8)\n",
    "ax.axvline(x=pd.to_datetime(cutoff) + pd.Timedelta('365 days'), c='gray', lw=4,\n",
    "           alpha=0.5, ls='--')\n",
    "ax.text(x=pd.to_datetime('2013-01-01'),y=6, s='Horizon', color='black',\n",
    "       fontsize=16, fontweight='bold', alpha=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The Prophet paper](https://peerj.com/preprints/3190.pdf) gives further description of simulated historical forecasts.\n",
    "\n",
    "This cross validation procedure can be done automatically for a range of historical cutoffs using the `cross_validation` function. We specify the forecast horizon (`horizon`), and then optionally the size of the initial training period (`initial`) and the spacing between cutoff dates (`period`). By default, the initial training period is set to three times the horizon, and cutoffs are made every half a horizon.\n",
    "\n",
    "The output of `cross_validation` is a dataframe with the true values `y` and the out-of-sample forecast values `yhat`, at each simulated forecast date and for each cutoff date. In particular, a forecast is made for every observed point between `cutoff` and `cutoff + horizon`. This dataframe can then be used to compute error measures of `yhat` vs. `y`.\n",
    "\n",
    "Here we do cross-validation to assess prediction performance on a horizon of 365 days, starting with 730 days of training data in the first cutoff and then making predictions every 180 days. On this 8 year time series, this corresponds to 11 total forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Making 11 forecasts with cutoffs between 2010-02-15 and 2015-01-20\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         y         ds     yhat yhat_lower yhat_upper     cutoff\n",
      "1 8.242493 2010-02-16 8.956252   8.451796   9.483227 2010-02-15\n",
      "2 8.008033 2010-02-17 8.722669   8.206799   9.174078 2010-02-15\n",
      "3 8.045268 2010-02-18 8.606339   8.136408   9.059811 2010-02-15\n",
      "4 7.928766 2010-02-19 8.528198   8.044159   9.012228 2010-02-15\n",
      "5 7.745003 2010-02-20 8.270095   7.787403   8.780680 2010-02-15\n",
      "6 7.866339 2010-02-21 8.601325   8.051539   9.112439 2010-02-15\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "df.cv <- cross_validation(m, initial = 730, period = 180, horizon = 365, units = 'days')\n",
    "head(df.cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4355ae2480f4f7cb37c613c84427237",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from prophet.diagnostics import cross_validation\n",
    "df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>y</th>\n",
       "      <th>cutoff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-16</td>\n",
       "      <td>8.959678</td>\n",
       "      <td>8.470035</td>\n",
       "      <td>9.451618</td>\n",
       "      <td>8.242493</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-17</td>\n",
       "      <td>8.726195</td>\n",
       "      <td>8.236734</td>\n",
       "      <td>9.219616</td>\n",
       "      <td>8.008033</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-02-18</td>\n",
       "      <td>8.610011</td>\n",
       "      <td>8.104834</td>\n",
       "      <td>9.125484</td>\n",
       "      <td>8.045268</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>8.532004</td>\n",
       "      <td>7.985031</td>\n",
       "      <td>9.041575</td>\n",
       "      <td>7.928766</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-20</td>\n",
       "      <td>8.274090</td>\n",
       "      <td>7.779034</td>\n",
       "      <td>8.745627</td>\n",
       "      <td>7.745003</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      yhat  yhat_lower  yhat_upper         y     cutoff\n",
       "0 2010-02-16  8.959678    8.470035    9.451618  8.242493 2010-02-15\n",
       "1 2010-02-17  8.726195    8.236734    9.219616  8.008033 2010-02-15\n",
       "2 2010-02-18  8.610011    8.104834    9.125484  8.045268 2010-02-15\n",
       "3 2010-02-19  8.532004    7.985031    9.041575  7.928766 2010-02-15\n",
       "4 2010-02-20  8.274090    7.779034    8.745627  7.745003 2010-02-15"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In R, the argument `units` must be a type accepted by `as.difftime`, which is weeks or shorter. In Python, the string for `initial`, `period`, and `horizon` should be in the format used by Pandas Timedelta, which accepts units of days or shorter.\n",
    "\n",
    "Custom cutoffs can also be supplied as a list of dates to the `cutoffs` keyword in the `cross_validation` function in Python and R. For example, three cutoffs six months apart, would need to be passed to the `cutoffs` argument in a date format like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "cutoffs <- as.Date(c('2013-02-15', '2013-08-15', '2014-02-15'))\n",
    "df.cv2 <- cross_validation(m, cutoffs = cutoffs, horizon = 365, units = 'days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd72143956e3426abd28c01b3a0b75b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "cutoffs = pd.to_datetime(['2013-02-15', '2013-08-15', '2014-02-15'])\n",
    "df_cv2 = cross_validation(m, cutoffs=cutoffs, horizon='365 days')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `performance_metrics` utility can be used to compute some useful statistics of the prediction performance (`yhat`, `yhat_lower`, and `yhat_upper` compared to `y`), as a function of the distance from the cutoff (how far into the future the prediction was). The statistics computed are mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), median absolute percent error (MDAPE) and coverage of the `yhat_lower` and `yhat_upper` estimates. These are computed on a rolling window of the predictions in `df_cv` after sorting by horizon (`ds` minus `cutoff`). By default 10% of the predictions will be included in each window, but this can be changed with the `rolling_window` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  horizon       mse      rmse       mae       mape      mdape      smape\n",
      "1 37 days 0.4945480 0.7032411 0.5055601 0.05858532 0.04934318 0.05887239\n",
      "2 38 days 0.5002964 0.7073163 0.5105297 0.05916131 0.04959087 0.05950813\n",
      "3 39 days 0.5225149 0.7228519 0.5166123 0.05975950 0.04934318 0.06023204\n",
      "4 40 days 0.5297905 0.7278671 0.5194979 0.06006795 0.04934318 0.06061026\n",
      "5 41 days 0.5372450 0.7329700 0.5204945 0.06014704 0.04923865 0.06075311\n",
      "6 42 days 0.5410136 0.7355363 0.5208659 0.06016509 0.04934318 0.06082444\n",
      "   coverage\n",
      "1 0.6815898\n",
      "2 0.6804477\n",
      "3 0.6777067\n",
      "4 0.6838739\n",
      "5 0.6914116\n",
      "6 0.6930105\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "df.p <- performance_metrics(df.cv)\n",
    "head(df.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horizon</th>\n",
       "      <th>mse</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mae</th>\n",
       "      <th>mape</th>\n",
       "      <th>mdape</th>\n",
       "      <th>smape</th>\n",
       "      <th>coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37 days</td>\n",
       "      <td>0.493764</td>\n",
       "      <td>0.702683</td>\n",
       "      <td>0.504754</td>\n",
       "      <td>0.058485</td>\n",
       "      <td>0.049922</td>\n",
       "      <td>0.058774</td>\n",
       "      <td>0.674052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38 days</td>\n",
       "      <td>0.499522</td>\n",
       "      <td>0.706769</td>\n",
       "      <td>0.509723</td>\n",
       "      <td>0.059060</td>\n",
       "      <td>0.049389</td>\n",
       "      <td>0.059409</td>\n",
       "      <td>0.672910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39 days</td>\n",
       "      <td>0.521614</td>\n",
       "      <td>0.722229</td>\n",
       "      <td>0.515793</td>\n",
       "      <td>0.059657</td>\n",
       "      <td>0.049540</td>\n",
       "      <td>0.060131</td>\n",
       "      <td>0.670169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40 days</td>\n",
       "      <td>0.528760</td>\n",
       "      <td>0.727159</td>\n",
       "      <td>0.518634</td>\n",
       "      <td>0.059961</td>\n",
       "      <td>0.049232</td>\n",
       "      <td>0.060504</td>\n",
       "      <td>0.671311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41 days</td>\n",
       "      <td>0.536078</td>\n",
       "      <td>0.732174</td>\n",
       "      <td>0.519585</td>\n",
       "      <td>0.060036</td>\n",
       "      <td>0.049389</td>\n",
       "      <td>0.060641</td>\n",
       "      <td>0.678849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  horizon       mse      rmse       mae      mape     mdape     smape  \\\n",
       "0 37 days  0.493764  0.702683  0.504754  0.058485  0.049922  0.058774   \n",
       "1 38 days  0.499522  0.706769  0.509723  0.059060  0.049389  0.059409   \n",
       "2 39 days  0.521614  0.722229  0.515793  0.059657  0.049540  0.060131   \n",
       "3 40 days  0.528760  0.727159  0.518634  0.059961  0.049232  0.060504   \n",
       "4 41 days  0.536078  0.732174  0.519585  0.060036  0.049389  0.060641   \n",
       "\n",
       "   coverage  \n",
       "0  0.674052  \n",
       "1  0.672910  \n",
       "2  0.670169  \n",
       "3  0.671311  \n",
       "4  0.678849  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from prophet.diagnostics import performance_metrics\n",
    "df_p = performance_metrics(df_cv)\n",
    "df_p.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validation performance metrics can be visualized with `plot_cross_validation_metric`, here shown for MAPE. Dots show the absolute percent error for each prediction in `df_cv`. The blue line shows the MAPE, where the mean is taken over a rolling window of the dots. We see for this forecast that errors around 5% are typical for predictions one month into the future, and that errors increase up to around 11% for predictions that are a year out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 10 -h 6 -u in\n",
    "plot_cross_validation_metric(df.cv, metric = 'mape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from prophet.plot import plot_cross_validation_metric\n",
    "fig = plot_cross_validation_metric(df_cv, metric='mape')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the rolling window in the figure can be changed with the optional argument `rolling_window`, which specifies the proportion of forecasts to use in each rolling window. The default is 0.1, corresponding to 10% of rows from `df_cv` included in each window; increasing this will lead to a smoother average curve in the figure. The `initial` period should be long enough to capture all of the components of the model, in particular seasonalities and extra regressors: at least a year for yearly seasonality, at least a week for weekly seasonality, etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallelizing cross validation\n",
    "\n",
    "Cross-validation can also be run in parallel mode in Python, by setting specifying the `parallel` keyword. Four modes are supported\n",
    "\n",
    "* `parallel=None` (Default, no parallelization)\n",
    "* `parallel=\"processes\"`\n",
    "* `parallel=\"threads\"`\n",
    "* `parallel=\"dask\"`\n",
    "\n",
    "For problems that aren't too big, we recommend using `parallel=\"processes\"`. It will achieve the highest performance when the parallel cross validation can be done on a single machine. For large problems, a [Dask](https://dask.org) cluster can be used to do the cross validation on many machines. You will need to [install Dask](https://docs.dask.org/en/latest/install.html) separately, as it will not be installed with `prophet`.\n",
    "\n",
    "\n",
    "```python\n",
    "from dask.distributed import Client\n",
    "\n",
    "client = Client()  # connect to the cluster\n",
    "df_cv = cross_validation(m, initial='730 days', period='180 days', horizon='365 days',\n",
    "                         parallel=\"dask\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning\n",
    "\n",
    "Cross-validation can be used for tuning hyperparameters of the model, such as `changepoint_prior_scale` and `seasonality_prior_scale`. A Python example is given below, with a 4x4 grid of those two parameters, with parallelization over cutoffs. Here parameters are evaluated on RMSE averaged over a 30-day horizon, but different performance metrics may be appropriate for different problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    changepoint_prior_scale  seasonality_prior_scale      rmse\n",
      "0                     0.001                     0.01  0.757694\n",
      "1                     0.001                     0.10  0.743399\n",
      "2                     0.001                     1.00  0.753387\n",
      "3                     0.001                    10.00  0.762890\n",
      "4                     0.010                     0.01  0.542315\n",
      "5                     0.010                     0.10  0.535546\n",
      "6                     0.010                     1.00  0.527008\n",
      "7                     0.010                    10.00  0.541544\n",
      "8                     0.100                     0.01  0.524835\n",
      "9                     0.100                     0.10  0.516061\n",
      "10                    0.100                     1.00  0.521406\n",
      "11                    0.100                    10.00  0.518580\n",
      "12                    0.500                     0.01  0.532140\n",
      "13                    0.500                     0.10  0.524668\n",
      "14                    0.500                     1.00  0.521130\n",
      "15                    0.500                    10.00  0.522980\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "param_grid = {  \n",
    "    'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],\n",
    "    'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0],\n",
    "}\n",
    "\n",
    "# Generate all combinations of parameters\n",
    "all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]\n",
    "rmses = []  # Store the RMSEs for each params here\n",
    "\n",
    "# Use cross validation to evaluate all parameters\n",
    "for params in all_params:\n",
    "    m = Prophet(**params).fit(df)  # Fit model with given params\n",
    "    df_cv = cross_validation(m, cutoffs=cutoffs, horizon='30 days', parallel=\"processes\")\n",
    "    df_p = performance_metrics(df_cv, rolling_window=1)\n",
    "    rmses.append(df_p['rmse'].values[0])\n",
    "\n",
    "# Find the best parameters\n",
    "tuning_results = pd.DataFrame(all_params)\n",
    "tuning_results['rmse'] = rmses\n",
    "print(tuning_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'changepoint_prior_scale': 0.1, 'seasonality_prior_scale': 0.1}\n"
     ]
    }
   ],
   "source": [
    "best_params = all_params[np.argmin(rmses)]\n",
    "print(best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, parallelization could be done across parameter combinations by parallelizing the loop above.\n",
    "\n",
    "The Prophet model has a number of input parameters that one might consider tuning. Here are some general recommendations for hyperparameter tuning that may be a good starting place.\n",
    "\n",
    "**Parameters that can be tuned**\n",
    "- `changepoint_prior_scale`: This is probably the most impactful parameter. It determines the flexibility of the trend, and in particular how much the trend changes at the trend changepoints. As described in this documentation, if it is too small, the trend will be underfit and variance that should have been modeled with trend changes will instead end up being handled with the noise term. If it is too large, the trend will overfit and in the most extreme case you can end up with the trend capturing yearly seasonality. The default of 0.05 works for many time series, but this could be tuned; a range of [0.001, 0.5] would likely be about right. Parameters like this (regularization penalties; this is effectively a lasso penalty) are often tuned on a log scale.\n",
    "\n",
    "- `seasonality_prior_scale`: This parameter controls the flexibility of the seasonality. Similarly, a large value allows the seasonality to fit large fluctuations, a small value shrinks the magnitude of the seasonality. The default is 10., which applies basically no regularization. That is because we very rarely see overfitting here (there's inherent regularization with the fact that it is being modeled with a truncated Fourier series, so it's essentially low-pass filtered). A reasonable range for tuning it would probably be [0.01, 10]; when set to 0.01 you should find that the magnitude of seasonality is forced to be very small. This likely also makes sense on a log scale, since it is effectively an L2 penalty like in ridge regression.\n",
    "\n",
    "- `holidays_prior_scale`: This controls flexibility to fit holiday effects. Similar to seasonality_prior_scale, it defaults to 10.0 which applies basically no regularization, since we usually have multiple observations of holidays and can do a good job of estimating their effects. This could also be tuned on a range of [0.01, 10] as with seasonality_prior_scale.\n",
    "\n",
    "- `seasonality_mode`: Options are [`'additive'`, `'multiplicative'`]. Default is `'additive'`, but many business time series will have multiplicative seasonality. This is best identified just from looking at the time series and seeing if the magnitude of seasonal fluctuations grows with the magnitude of the time series (see the documentation here on multiplicative seasonality), but when that isn't possible, it could be tuned.\n",
    "\n",
    "**Maybe tune?**\n",
    "- `changepoint_range`: This is the proportion of the history in which the trend is allowed to change. This defaults to 0.8, 80% of the history, meaning the model will not fit any trend changes in the last 20% of the time series. This is fairly conservative, to avoid overfitting to trend changes at the very end of the time series where there isn't enough runway left to fit it well. With a human in the loop, this is something that can be identified pretty easily visually: one can pretty clearly see if the forecast is doing a bad job in the last 20%. In a fully-automated setting, it may be beneficial to be less conservative. It likely will not be possible to tune this parameter effectively with cross validation over cutoffs as described above. The ability of the model to generalize from a trend change in the last 10% of the time series will be hard to learn from looking at earlier cutoffs that may not have trend changes in the last 10%. So, this parameter is probably better not tuned, except perhaps over a large number of time series. In that setting, [0.8, 0.95] may be a reasonable range.\n",
    "\n",
    "**Parameters that would likely not be tuned**\n",
    "- `growth`: Options are 'linear' and 'logistic'. This likely will not be tuned; if there is a known saturating point and growth towards that point it will be included and the logistic trend will be used, otherwise it will be linear.\n",
    "\n",
    "- `changepoints`: This is for manually specifying the locations of changepoints. None by default, which automatically places them.\n",
    "\n",
    "- `n_changepoints`: This is the number of automatically placed changepoints. The default of 25 should be plenty to capture the trend changes in a typical time series (at least the type that Prophet would work well on anyway). Rather than increasing or decreasing the number of changepoints, it will likely be more effective to focus on increasing or decreasing the flexibility at those trend changes, which is done with `changepoint_prior_scale`.\n",
    "\n",
    "- `yearly_seasonality`: By default ('auto') this will turn yearly seasonality on if there is a year of data, and off otherwise. Options are ['auto', True, False]. If there is more than a year of data, rather than trying to turn this off during HPO, it will likely be more effective to leave it on and turn down seasonal effects by tuning `seasonality_prior_scale`.\n",
    "\n",
    "- `weekly_seasonality`: Same as for `yearly_seasonality`.\n",
    "\n",
    "- `daily_seasonality`: Same as for `yearly_seasonality`.\n",
    "\n",
    "- `holidays`: This is to pass in a dataframe of specified holidays. The holiday effects would be tuned with `holidays_prior_scale`.\n",
    "\n",
    "- `mcmc_samples`: Whether or not MCMC is used will likely be determined by factors like the length of the time series and the importance of parameter uncertainty (these considerations are described in the documentation).\n",
    "\n",
    "- `interval_width`: Prophet `predict` returns uncertainty intervals for each component, like `yhat_lower` and `yhat_upper` for the forecast `yhat`. These are computed as quantiles of the posterior predictive distribution, and `interval_width` specifies which quantiles to use. The default of 0.8 provides an 80% prediction interval. You could change that to 0.95 if you wanted a 95% interval. This will affect only the uncertainty interval, and will not change the forecast `yhat` at all and so does not need to be tuned.\n",
    "\n",
    "- `uncertainty_samples`: The uncertainty intervals are computed as quantiles from the posterior predictive interval, and the posterior predictive interval is estimated with Monte Carlo sampling. This parameter is the number of samples to use (defaults to 1000). The running time for predict will be linear in this number. Making it smaller will increase the variance (Monte Carlo error) of the uncertainty interval, and making it larger will reduce that variance. So, if the uncertainty estimates seem jagged this could be increased to further smooth them out, but it likely will not need to be changed. As with `interval_width`, this parameter only affects the uncertainty intervals and changing it will not affect in any way the forecast `yhat`; it does not need to be tuned."
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